Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/234334
Title: Synergistic Fibroblast Optimization A Novel Nature Inspired Computing Algorithm
Researcher: Dhivyaprabha T T
Guide(s): Subashini P
Keywords: Engineering and Technology,Computer Science,Computer Science Information Systems
University: Avinashilingam Deemed University For Women
Completed Date: 14-09-2018
Abstract: Nature Inspired Computing (NIC) paradigms are developed by inspiring natural mechanism or newlinenatural principle as source of metaphor includes evolution, ecology, development and behaviour for newlineproblem solving. The taxonomy of NIC paradigms are partitioned into Evolutionary Computation newline(EC) and Swarm Intelligence (SI) techniques, in which, both are population based global optimization newlinealgorithms. Genetic Algorithm (GA) is a typical Evolutionary Algorithm (EA) which is inspired from newlineDarwin evolution theory. On the other hand, Swarm Intelligence (SI) defines collective behaviour of newlineagents or population based natural systems have been developed. The performance of global newlineoptimization techniques for solving the large scale of non-linear complex problems implied that newlineParticle Swarm Optimization (PSO) is the most popular metaheuristic algorithm which inspired many newlineresearchers due to its characteristics, such as, cooperation, topology, searching mechanism, social newlinebehaviour, interaction and mobility and the significant outcomes determines its efficiency. From the newlineexperiential analysis on the exploration and exploitation behaviours delivered by PSO algorithm and newlinevariants of PSOs, it is identified that, the premature convergence problem suffered by PSO algorithm newlineand variant of PSOs leads to candidate solution being trapped in the local optimum (stagnation newlineproblem). It affected the movement behaviour of particles to deviate from local optima and exhibit newlinepoor performance in solving highly complex problems. It reveals that introducing a new mechanism newlineinspired by natural phenomena has significantly improved the efficiency of PSO. Henceforth, these newlineinferential ideas laid down a foundation to acquire knowledge derived from the biological phenomena newlineof cellular organism to introduce new parameter(s) in PSO algorithm that improves its efficiency to a newlinegreat extent. The intellectual behaviour delivered by fibroblast in the wound healing process has newlinemotivated to design and develop Synergistic Fibroblast Optimization (SFO) algorithm.
Pagination: 122 p.
URI: http://hdl.handle.net/10603/234334
Appears in Departments:Department of Computer Science

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02_certificate.pdf305.41 kBAdobe PDFView/Open
03_acknowledgement.pdf115.9 kBAdobe PDFView/Open
04_contents.pdf47.64 kBAdobe PDFView/Open
05_abstract.pdf111.06 kBAdobe PDFView/Open
06_chapter 1.pdf4.44 MBAdobe PDFView/Open
07_chapter 2.pdf4.99 MBAdobe PDFView/Open
08_chapter 3.pdf4.46 MBAdobe PDFView/Open
09_chapter 4.pdf6.7 MBAdobe PDFView/Open
10_chapter 5.pdf4.46 MBAdobe PDFView/Open
11_chapter 6.pdf4.51 MBAdobe PDFView/Open
12_references.pdf277.94 kBAdobe PDFView/Open


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